Deep learning-based prediction of enhanced CT scans for lymph node metastasis in esophageal squamous cell carcinoma.

Background: Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge with a particularly grim prognosis. Accurate prediction of lymph node metastasis (LNM) in ESCC is crucial for optimizing treatment strategies and improving patient outcomes. This study leverages the power of deep learning, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze arterial phase enhanced CT images and predict LNM in ESCC patients.

Combination of Deep Learning Grad-CAM and Radiomics for Automatic Localization and Diagnosis of Architectural Distortion on DBT.

Rationale And Objectives: Detection and diagnosis of architectural distortion (AD) on digital breast tomosynthesis (DBT) is challenging. This study applied artificial intelligence (AI) using deep learning (DL) algorithms to detect AD, followed by radiomics for classification.

Materials And Methods: 500 cases with AD on DBT reports were identified; the earlier 292 cases for training, and the later 208 cases for testing.

Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study.

Objectives: To explore the performance differences of multiple annotations in radiomics analysis and provide a reference for tumour annotation in large-scale medical image analysis.

Methods: A total of 342 patients from two centres who underwent radical resection for rectal cancer were retrospectively studied and divided into training, internal validation, and external validation cohorts. Three predictive tasks of tumour T-stage (pT), lymph node metastasis (pLNM), and disease-free survival (pDFS) were performed.

Characterization of local white matter microstructural alterations in Alzheimer's disease: A reproducible study.

Alzheimer's disease (AD) is a neurodegenerative disease with a close association with microstructural alterations in white matter (WM). Current studies lack the characterization and further validation of specific regions in WM fiber tracts in AD. This study subdivided fiber tracts into multiple fiber clusters on the basis of automated fiber clustering and performed quantitative analysis along the fiber clusters to identify local WM microstructural alterations in AD.

Radiation pneumonitis prediction with dual-radiomics for esophageal cancer underwent radiotherapy.

Background: To integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT).

Methods: Total of 143 EC patients in the authors' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.

Low blood flow ratio is associated with hemorrhagic transformation secondary to mechanical thrombectomy in patients with acute ischemic stroke.

Background And Purpose: A significant decrease of cerebral blood flow (CBF) is a risk factor for hemorrhagic transformation (HT) in acute ischemic stroke (AIS). This study aimed to ascertain whether the ratio of different CBF thresholds derived from computed tomography perfusion (CTP) is an independent risk factor for HT after mechanical thrombectomy (MT).

Methods: A retrospective single center cohort study was conducted on patients with AIS undergoing MT at the First Affiliated Hospital of Wenzhou Medical University from August 2018 to December 2023.

Histone 3 lysine 9 acetylation-specific reprogramming regulates esophageal squamous cell carcinoma progression and metastasis.

Dysregulation of histone acetylation is widely implicated in tumorigenesis, yet its specific roles in the progression and metastasis of esophageal squamous cell carcinoma (ESCC) remain unclear. Here, we profiled the genome-wide landscapes of H3K9ac for paired adjacent normal (Nor), primary ESCC (EC) and metastatic lymph node (LNC) esophageal tissues from three ESCC patients. Compared to H3K27ac, we identified a distinct epigenetic reprogramming specific to H3K9ac in EC and LNC samples relative to Nor samples.

Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach.

Background: Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. Despite this, current techniques, which can be invasive and costly, struggle with achieving the necessary precision, highlighting a pressing need for more effective, non-invasive alternatives.

Method: We developed ResoLSTM-Depth, a deep learning model to distinguish ESCC stages T1-T2 from T3-T4.

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

Background: Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear.

Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning.

Purpose: To implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images.

Materials And Methods: A total of 298 patients were identified from a retrospective review, and all of them had confirmed pathological diagnoses, 175 malignant and 123 benign. The BI-RADS scores of DBT were obtained from the radiology reports, classified into 2, 3, 4A, 4B, 4C, and 5.

Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification.

Objective: To develop a multi-modality radiomics nomogram based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions.

Material And Methods: In this retrospective study, 345 breast lesions from 305 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n = 241) and testing (n = 104) datasets. Radiomics features were extracted from manually contoured images.

Differentiating Primary Tumors for Brain Metastasis with Integrated Radiomics from Multiple Imaging Modalities.

Objectives: To differentiate the primary site of brain metastases (BMs) is of high clinical value for the successful management of patients with BM. The purpose of this study is to investigate a combined radiomics model with computer tomography (CT) and magnetic resonance imaging (MRI) images in differentiating BMs originated from lung and breast cancer.

Methods: Pretreatment cerebral contrast enhanced CT and T1-weighted MRI images of 78 patients with 179 BMs from primary lung and breast cancer were retrospectively analyzed.

Dynamic change of COVID-19 lung infection evaluated using co-registration of serial chest CT images.

Purpose: To evaluate the volumetric change of COVID-19 lesions in the lung of patients receiving serial CT imaging for monitoring the evolution of the disease and the response to treatment.

Materials And Methods: A total of 48 patients, 28 males and 20 females, who were confirmed to have COVID-19 infection and received chest CT examination, were identified. The age range was 21-93 years old, with a mean of 54 ± 18 years.

Image Quality Control in Lumbar Spine Radiography Using Enhanced U-Net Neural Networks.

Purpose: To standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.

Materials And Methods: A dataset comprising anteroposterior, lateral, and oblique position lumbar spine x-ray images from 1,389 patients was analyzed in this study. The training set consisted of digital radiography images of 1,070 patients (800, 798, and 623 images of the anteroposterior, lateral, and oblique position, respectively) and the validation set included 319 patients (200, 205, and 156 images of the anteroposterior, lateral, and oblique position, respectively).

Brain network connectivity feature extraction using deep learning for Alzheimer's disease classification.

Early diagnosis and therapeutic intervention for Alzheimer's disease (AD) is currently the only viable option for improving clinical outcomes. Combining structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) to diagnose AD has yielded promising results. Most studies assume fixed time lags when constructing functional networks.

Integrative Analysis of 5-Hydroxymethylcytosine and Transcriptional Profiling Identified 5hmC-Modified lncRNA Panel as Non-Invasive Biomarkers for Diagnosis and Prognosis of Pancreatic Cancer.

Pancreatic adenocarcinoma (PAAD) is the fourth leading cause of cancer-related deaths worldwide. 5-Hydroxymethylcytosine (5hmC)-mediated epigenetic regulation has been reported to be involved in cancer pathobiology and has emerged to be promising biomarkers for cancer diagnosis and prognosis. However, 5hmC alterations at long non-coding RNA (lncRNA) genes and their clinical significance remained unknown.

Molecular Mechanism Exploration of Pancreatitis Based on miRNA Expression Profile.

Background: Pancreatitis is a popular disease around the world, and can also lead to pancreatic cancer. Pancreatitis can be distinguished into two types, acute pancreatitis (AP) and chronic pancreatitis (CP). Every year, AP leads to approximately 275,000 new cases and is the most frequent gastrointestinal disease in American.

Reduced artifacts and improved diagnostic value of 640-slice computed tomography in patients with cardiac pacemakers.

Objective: The aim of this study was to compare the feasibility of 640-slice with 64-slice computed tomography (CT) coronary angiography for diagnosing coronary lesions in patients with pacemakers.

Methods: Forty-five and 50 patients with pacemakers and with suspected or known coronary artery disease underwent 64-slice (64 group) and 640-slice (640 group) CT scans, respectively. All segments of the vessels were evaluated according to the 15-segment model recommended by the American Heart Association.

Spectral CT Analysis of Solitary Pulmonary Nodules for Differentiating Malignancy from Benignancy: The Value of Iodine Concentration Spatial Distribution Difference.

Objective: The objective is to assess the value of spatial distribution difference in iodine concentration between malignant and benign solitary pulmonary nodules (SPNs) by analyzing multiple parameters of spectral CT.

Methods: Sixty patients with 39 malignant nodules and 21 benign nodules underwent chest contrast CT scans using spectral imaging mode during pulmonary arterial phase (PP), arterial phase (AP), and venous phase (VP). Iodine concentrations of proximal and distal regions in pulmonary nodules on iodine-based material decomposition images were recorded.

Whole-brain CT perfusion imaging using increased sampling intervals: A pilot study.

The aim of the present study was to investigate the feasibility of whole-brain perfusion imaging using the increased sampling interval protocol for 320-detector row dynamic-volume computed tomography (CT). A total of 12 volunteers were recruited. The novel protocols with 11 volumes (defined as protocol P11) and 15 volumes (defined as protocol P15) were performed on the volunteers to evaluate whether P11 and P15 are able to acquire comparable results to the standard protocol with 19 volumes (defined as protocol P19) according to the as-low-as-reasonably-achievable principle.

Quantitative assessment on blood-brain barrier permeability of acute spontaneous intracerebral hemorrhage in basal ganglia: a CT perfusion study.

Purpose: Blood-brain barrier (BBB) damage aggravates perihematomal edema, and edema volume predicts prognosis independently. But the BBB permeability at the late stage of acute intracerebral hemorrhage (ICH) patients is uncertain. We aimed to assess the BBB permeability of spontaneous basal ganglia ICH using computed tomographic perfusion (CTP) and investigates its relationship with hematoma and perihematomal edema volume.

Surgical resection of pulmonary metastases from colorectal cancer: 11 years of experiences.

Objective: To analyze the benefits and prognostic factors after surgical resection of pulmonary metastases from colorectal cancer (CRC).

Methods: From Jan. 2004 to Jan.

Integrated analysis of mRNA and miRNA expression profiles in pancreatic ductal adenocarcinoma.

In the present study, to investigate the potential molecular mechanism of pancreatic ductal adenocarcinoma (PDAC), mRNA and miRNA expression profiles were integrated for systematic analysis. Results showed that a total of 76 common differentially expressed genes (DEGs) were identified from 2 mRNA expression profiles that contained 39 tumor and 15 normal samples. Notably, the tumor and normal samples were able to be clearly classified into 4 groups based on the DEGs.

Effect of alprazolam on rat serum metabolic profiles.

We developed a serum metabolomic method by gas chromatography-mass spectrometry (GC-MS) to evaluate the effect of alprazolam in rats. The GC-MS with HP-5MS (0.25 μm × 30 m × 0.